Anticipatory vehicle headlight actuation

ABSTRACT

Anticipatory vehicle electronic control unit (ECU) actuation, including: determining, based on sensor data capturing an environment around a vehicle, a predicted environmental state corresponding to a future time and comprising a transition in exterior lighting; determining, based on the predicted environmental state, an actuation state for one or more headlights of the vehicle; and configuring the one or more headlights of the vehicle according to the actuation state before the future time.

BACKGROUND Field of the Invention

The field of the invention is vehicle systems, or, more specifically, methods, apparatus, autonomous vehicles, and products for anticipatory vehicle electronic control unit (ECU) actuation.

Description of Related Art

Existing solutions for ECU actuation are reactionary in that particular ECUs are actuated based on presently observed environmental or driving behavior. In other words, ECUs are activated in response to satisfying or reaching detected conditions for actuating the particular ECUs.

SUMMARY

Anticipatory vehicle electronic control unit (ECU) actuation may include: determining, based on sensor data capturing an environment around a vehicle, a predicted environmental state corresponding to a future time; determining, based on the predicted environmental state, an actuation state for one or more ECUs of the vehicle; and configuring the one or more ECUs according to the actuation state before the future time.

The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows example views of an autonomous vehicle for anticipatory vehicle electronic control unit (ECU) actuation according to some embodiments of the present disclosure according to some embodiments of the present disclosure.

FIG. 2 is a block diagram of an autonomous computing system for anticipatory vehicle ECU actuation according to some embodiments of the present disclosure.

FIG. 3 is a block diagram of a redundant power fabric for anticipatory vehicle ECU actuation according to some embodiments of the present disclosure.

FIG. 4 is a block diagram of a redundant data fabric for anticipatory vehicle ECU actuation according to some embodiments of the present disclosure.

FIG. 5 is an example view of process allocation across CPU packages for anticipatory vehicle ECU actuation according to some embodiments of the present disclosure.

FIG. 6 is an example view of an execution environment for anticipatory vehicle ECU actuation according to some embodiments of the present disclosure.

FIG. 7 is a flowchart of an example method for anticipatory vehicle ECU actuation according to some embodiments of the present disclosure.

FIG. 8 is a flowchart of an example method for anticipatory vehicle ECU actuation according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The terminology used herein for the purpose of describing particular examples is not intended to be limiting for further examples. Whenever a singular form such as “a,” “an,” and “the” is used and using only a single element is neither explicitly or implicitly defined as being mandatory, further examples may also use plural elements to implement the same functionality. Likewise, when a functionality is subsequently described as being implemented using multiple elements, further examples may implement the same functionality using a single element or processing entity. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used, specify the presence of the stated features, integers, steps, operations, processes, acts, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, processes, acts, elements, components, and/or any group thereof. Additionally, when an element is described as “plurality,” it is understood to mean two or more of such an element. However, as set forth above, further examples may implement the same functionality using a single element.

It will be understood that when an element is referred to as being “connected” or “coupled” to another element, the elements may be directly connected or coupled or via one or more intervening elements. If two elements A and B are combined using an “or,” this is to be understood to disclose all possible combinations, i.e. only A, only B, as well as A and B. An alternative wording for the same combinations is “at least one of A and B.” The same applies for combinations of more than two elements.

Accordingly, while further examples are capable of various modifications and alternative forms, some particular examples thereof are shown in the figures and will subsequently be described in detail. However, this detailed description does not limit further examples to the particular forms described. Further examples may cover all modifications, equivalents, and alternatives falling within the scope of the disclosure. Like numbers refer to like or similar elements throughout the description of the figures, which may be implemented identically or in modified form when compared to one another while providing for the same or a similar functionality.

[PREAMBLE] may be implemented in an autonomous vehicle. Accordingly, FIG. 1 shows multiple views of an autonomous vehicle 100 configured for [PREAMBLE] according to embodiments of the present disclosure. Right side view 101 a shows a right side of the autonomous vehicle 100. Shown in the right-side view 101 a are cameras 102 and 103, configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle 100 from the perspective of the right side of the car. Front view 101 b shows a front side of the autonomous vehicle 100. Shown in the front view 101 b are cameras 104 and 106, configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle 100 from the perspective of the front of the car. Rear view 101 c shows a rear side of the autonomous vehicle 100. Shown in the rear view 101 c are cameras 108 and 110, configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle 100 from the perspective of the rear of the car. Top view 101 d shows a rear side of the autonomous vehicle 100. Shown in the top view 101 d are cameras 102-110. Also shown are cameras 112 and 114, configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle 100 from the perspective of the left side of the car.

Further shown in the top view 101 d is an automation computing system 116. The automation computing system 116 comprises one or more computing devices configured to control one or more autonomous operations (e.g., autonomous driving operations) of the autonomous vehicle 100. For example, the automation computing system 116 may be configured to process sensor data (e.g., data from the cameras 102-114 and potentially other sensors), operational data (e.g., a speed, acceleration, gear, orientation, turning direction), and other data to determine an operational state and/or operational history of the autonomous vehicle. The automation computing system 116 may then determine one or more operational commands for the autonomous vehicle (e.g., a change in speed or acceleration, a change in brake application, a change in gear, a change in turning or orientation). The automation computing system 116 may also capture and store sensor data. Operational data of the autonomous vehicle may also be stored in association with corresponding sensor data, thereby indicating the operational data of the autonomous vehicle 100 at the time the sensor data was captured.

Although the autonomous vehicle 100 if FIG. 1 is shown as car, it is understood that autonomous vehicles 100 configured for [PREAMBLE] may also include other vehicles, including motorcycles, planes, helicopters, unmanned aerial vehicles (UAVs, e.g., drones), or other vehicles. Moreover, it is understood that additional cameras or other external sensors may also be included in the autonomous vehicle 100.

[PREAMBLE] in accordance with the present disclosure is generally implemented with computers, that is, with automated computing machinery. For further explanation, therefore, FIG. 2 sets forth a block diagram of automated computing machinery comprising an exemplary automation computing system 116 configured for [PREAMBLE] according to specific embodiments. The automation computing system 116 of FIG. 2 includes at least one computer Central Processing Unit (CPU) package 204 as well as random access memory 206 (RAM′) which is connected through a high-speed memory bus 208 and bus adapter 210 to CPU packages 204 via a front side bus 211 and to other components of the automation computing system 116.

A CPU package 204 may comprise a plurality of processing units. For example, each CPU package 204 may comprise a logical or physical grouping of a plurality of processing units. Each processing unit may be allocated a particular process for execution. Moreover, each CPU package 204 may comprise one or more redundant processing units. A redundant processing unit is a processing unit not allocated a particular process for execution unless a failure occurs in another processing unit. For example, when a given processing unit allocated a particular process fails, a redundant processing unit may be selected and allocated the given process. A process may be allocated to a plurality of processing units within the same CPU package 204 or different CPU packages 204. For example, a given process may be allocated to a primary processing unit in a CPU package 204. The results or output of the given process may be output from the primary processing unit to a receiving process or service. The given process may also be executed in parallel on a secondary processing unit. The secondary processing unit may be included within the same CPU package 204 or a different CPU package 204. The secondary processing unit may not provide its output or results of the process until the primary processing unit fails. The receiving process or service will then receive data from the secondary processing unit. A redundant processing unit may then be selected and have allocated the given process to ensure that two or more processing units are allocated the given process for redundancy and increased reliability.

The CPU packages 204 are communicatively coupled to one or more sensors 212. The sensors 212 are configured to capture sensor data describing the operational and environmental conditions of an autonomous vehicle. For example, the sensors 212 may include cameras (e.g., the cameras 102-114 of FIG. 1 ), accelerometers, Global Positioning System (GPS) radios, Lidar sensors, or other sensors. As described herein, cameras may include a solid state sensor 212 with a solid-state shutter capable of measuring photons or a time of flight of photons. For example, a camera may be configured to capture or measure photons captured via the shutter for encoding as images and/or video data. As another example, a camera may emit photons and measure the time of flight of the emitted photons. Cameras may also include event cameras configured to measure changes in light and/or motion of light.

Although the sensors 212 are shown as being external to the automation computing system 116, it is understood that one or more of the sensors 212 may reside as a component of the automation computing system 116 (e.g., on the same board, within the same housing or chassis). The sensors 212 may be communicatively coupled with the CPU packages 204 via a switched fabric 213. The switched fabric 213 comprises a communications topology through which the CPU packages 204 and sensors 212 are coupled via a plurality of switching mechanisms (e.g., latches, switches, crossbar switches, field programmable gate arrays (FPGAs)). For example, the switched fabric 213 may implement a mesh connection connecting the CPU packages 204 and sensors 212 as endpoints, with the switching mechanisms serving as intermediary nodes of the mesh connection. The CPU packages 204 and sensors 212 may be in communication via a plurality of switched fabrics 213. For example, each of the switched fabrics 213 may include the CPU packages 204 and sensors 212, or a subset of the CPU packages 204 and sensors 212, as endpoints. Each switched fabric 213 may also comprise a respective plurality of switching components. The switching components of a given switched fabric 213 may be independent (e.g., not connected) of the switching components of other switched fabrics 213 such that only switched fabric 213 endpoints (e.g., the CPU packages 204 and sensors 212) are overlapping across the switched fabrics 213. This provides redundancy such that, should a connection between a CPU package 204 and sensor 212 fail in one switched fabric 213, the CPU package 204 and sensor 212 may remain connected via another switched fabric 213. Moreover, in the event of a failure in a CPU package 204, a processor of a CPU package 204, or a sensor, a communications path excluding the failed component and including a functional redundant component may be established.

The CPU packages 204 and sensors 212 are configured to receive power from one or more power supplies 215. The power supplies 215 may comprise an extension of a power system of the autonomous vehicle 100 or an independent power source (e.g., a battery, a capacitor). The power supplies 215 may supply power to the CPU packages 204 and sensors 212 by another switched fabric 214. The switched fabric 214 provides redundant power pathways such that, in the event of a failure in a power connection, a new power connection pathway may be established to the CPU packages 204 and sensors 212.

Stored in RAM 206 is an automation module 220. The automation module 220 may be configured to process sensor data from the sensors 212 to determine a driving decision for the autonomous vehicle. The driving decision comprises one or more operational commands for an autonomous vehicle 100 to affect the movement, direction, or other function of the autonomous vehicle 100, thereby facilitating autonomous driving or operation of the vehicle. Such operational commands may include a change in the speed of the autonomous vehicle 100, a change in steering direction, a change in gear, or other commands. For example, the automation module 220 may provide sensor data and/or processed sensor data as one or more inputs to a trained machine learning model (e.g., a trained neural network) to determine the one or more operational commands. The operational commands may then be communicated to autonomous vehicle control systems 223 via a vehicle interface 222.

In some embodiments, the automation module 220 may be configured to determine an exit path for an autonomous vehicle 100 in motion. The exit path includes one or more operational commands that, if executed, are determined and/or predicted to bring the autonomous vehicle 100 safely to a stop (e.g., without collision with an object, without violating one or more safety rules). The automation module 220 may determine both a driving decision and an exit path at a predefined interval. The automation module 220 may then send the driving decision and the exit path to the autonomous vehicle control systems 223. The autonomous vehicle control systems 223 may be configured to execute the driving decision unless an error state has been reached. If an error decision has been reached, therefore indicating a possible error in functionality of the automation computing system 116, the autonomous vehicle control systems 223 may then execute a last received exit path in order to bring the autonomous vehicle 100 safely to a stop. Thus, the autonomous vehicle control systems 223 are configured to receive both a driving decision and exit path at predefined intervals, and execute the exit path in response to an error.

The autonomous vehicle control systems 223 are configured to affect the movement and operation of the autonomous vehicle 100. For example, the autonomous vehicle control systems 223 may activate (e.g., apply one or more control signals) to actuators or other components to turn or otherwise change the direction of the autonomous vehicle 100, accelerate or decelerate the autonomous vehicle 100, change a gear of the autonomous vehicle 100, or otherwise affect the movement and operation of the autonomous vehicle 100.

Further stored in RAM 206 is a data collection module 224 configured to process and/or store sensor data received from the one or more sensors 212. For example, the data collection module 224 may store the sensor data as captured by the one or more sensors 212, or processed sensor 212 data (e.g., sensor 212 data having object recognition, compression, depth filtering, or any combination of these). Such processing may be performed by the data collection module 224 in real-time or in substantially real-time as the sensor data is captured by the one or more sensors 212. The processed sensor data may then be used by other functions or modules. For example, the automation module 220 may use processed sensor data as input to determine one or more operational commands. The data collection module 224 may store the sensor data in data storage 218.

Also stored in RAM 206 is a data processing module 226. The data processing module 226 is configured to perform one or more processes on stored sensor data (e.g., stored in data storage 218 by the data collection module 218) prior to upload to an execution environment 227. Such operations can include filtering, compression, encoding, decoding, or other operations. The data processing module 226 may then communicate the processed and stored sensor data to the execution environment 227.

Further stored in RAM 206 is a hypervisor 228. The hypervisor 228 is configured to manage the configuration and execution of one or more virtual machines 229. For example, each virtual machine 229 may emulate and/or simulate the operation of a computer. Accordingly, each virtual machine 229 may comprise a guest operating system 216 for the simulated computer. Each instance of virtual machine 229 may host the same operating system or one or more different operating systems. The hypervisor 228 may manage the creation of a virtual machine 229 including installation of the guest operating system 216. The hypervisor 228 may also manage when execution of a virtual machine 229 begins, is suspended, is resumed, or is terminated. The hypervisor 228 may also control access to computational resources (e.g., processing resources, memory resources, device resources) by each of the virtual machines.

Each of the virtual machines 229 may be configured to execute one or more of the automation modules 220, the data collection module 224, the data processing module 226, or combinations thereof. Moreover, as is set forth above, each of the virtual machines 229 may comprise its own guest operating system 216. Guest operating systems 216 useful in autonomous vehicles in accordance with some embodiments of the present disclosure include UNIX™, Linux™, Microsoft Windows™, AIX™, IBM's i OS™, and others. For example, the autonomous vehicle 100 may be configured to execute a first operating system when the autonomous vehicle is in an autonomous (or partially autonomous) driving mode and the autonomous vehicle 100 may be configured to execute a second operating system when the autonomous vehicle is not in an autonomous (or partially autonomous) driving mode. In such an example, the first operating system may be formally verified, secure, and operate in real-time such that data collected from the sensors 212 are processed within a predetermined period of time, and autonomous driving operations are performed within a predetermined period of time, such that data is processed and acted upon essentially in real-time. Continuing with this example, the second operating system may not be formally verified, may be less secure, and may not operate in real-time as the tasks that are carried out (which are described in greater detail below) by the second operating system are not as time-sensitive the tasks (e.g., carrying out self-driving operations) performed by the first operating system.

Although the example included in the preceding paragraph relates to an embodiment where the autonomous vehicle 100 may be configured to execute a first operating system when the autonomous vehicle is in an autonomous (or partially autonomous) driving mode and the autonomous vehicle 100 may be configured to execute a second operating system when the autonomous vehicle is not in an autonomous (or partially autonomous) driving mode, other embodiments are within the scope of the present disclosure. For example, in another embodiment one CPU (or other appropriate entity such as a chip, CPU core, and so on) may be executing the first operating system and a second CPU (or other appropriate entity) may be executing the second operating system, where switching between these two modalities is accomplished through fabric switching, as described in greater detail below. Likewise, in some embodiments, processing resources such as a CPU may be partitioned where a first partition supports the execution of the first operating system and a second partition supports the execution of the second operating system.

The guest operating systems 216 may correspond to a particular operating system modality. An operating system modality is a set of parameters or constraints which a given operating system satisfies, and are not satisfied by operating systems of another modality. For example, a given operating system may be considered a “real-time operating system” in that one or more processes executed by the operating system must be performed according to one or more time constraints. The time constraint may not necessarily be in real-time, but instead with the highest or one of the highest priorities so that operations indicated for a real-time modality are executed faster than operations without such a priority. For example, as the automation module 220 must make determinations as to operational commands to facilitate autonomous operation of a vehicle. Accordingly, the automation module 220 must make such determinations within one or more time constraints in order for autonomous operation to be performed in real-time. The automation module 220 may then be executed in an operating system (e.g., a guest operating system 216 of a virtual machine 229) corresponding to a “real-time operating system” modality. Conversely, the data processing module 226 may be able to perform its processing of sensor data independent of any time constraints, and may then be executed in an operating system (e.g., a guest operating system 216 of a virtual machine 229) corresponding to a “non-real-time operating system” modality.

As another example, an operating system (e.g., a guest operating system 216 of a virtual machine 229) may comprise a formally verified operating system. A formally verified operating system is an operating system for which the correctness of each function and operation has been verified with respect to a formal specification according to formal proofs. A formally verified operating system and an unverified operating system (e.g., one that has not been formally verified according to these proofs) can be said to operate in different modalities.

The automation module 220, data collection module 224, data collection module 224, data processing module 226, hypervisor 228, and virtual machine 229 in the example of FIG. 2 are shown in RAM 206, but many components of such software typically are stored in non-volatile memory also, such as, for example, on data storage 218, such as a disk drive. Moreover, any of the automation module 220, data collection module 224, and data processing module 226 may be executed in a virtual machine 229 and facilitated by a guest operating system 216 of that virtual machine 229.

The automation computing system 116 of FIG. 2 includes disk drive adapter 230 coupled through expansion bus 232 and bus adapter 210 to CPU package(s) 204 and other components of the automation computing system 116. Disk drive adapter 230 connects non-volatile data storage to the automation computing system 116 in the form of data storage 218. Disk drive adapters 230 useful in computers configured for [PREAMBLE] according to various embodiments include Integrated Drive Electronics (IDE′) adapters, Small Computer System Interface (SCSI′) adapters, and others. Non-volatile computer memory also may be implemented for as an optical disk drive, electrically erasable programmable read-only memory (so-called ‘EEPROM’ or ‘Flash’ memory), RAM drives, and so on.

The exemplary automation computing system 116 of FIG. 2 includes a communications adapter 238 for data communications with other computers and for data communications with a data communications network. Such data communications may be carried out serially through RS-238 connections, through external buses such as a Universal Serial Bus (‘USB’), through data communications networks such as IP data communications networks, and in other ways. Communications adapters implement the hardware level of data communications through which one computer sends data communications to another computer, directly or through a data communications network. Examples of communications adapters useful in computers configured for [PREAMBLE] according to specific embodiments include modems for wired dial-up communications, Ethernet (IEEE 802.3) adapters for wired data communications, 802.11 adapters for wireless data communications, as well as mobile adapters (e.g., cellular communications adapters) for mobile data communications. For example, the automation computing system 116 may communicate with one or more remotely disposed execution environments 227 via the communications adapter 238.

The exemplary automation computing system of FIG. 2 also includes one or more Artificial Intelligence (AI) accelerators 240. The AI accelerator 240 provides hardware-based assistance and acceleration of AI-related functions, including machine learning, computer vision, etc. Accordingly, performance of any of the automation module 220, data collection module 224, data processing module 226, or other operations of the automation computing system 116 may be performed at least in part by the AI accelerators 240.

The exemplary automation computing system of FIG. 2 also includes one or more graphics processing units (GPUs) 242. The GPUs 242 are configured to provide additional processing and memory resources for processing image and/or video data, including encoding, decoding, etc. Accordingly, performance of any of the automation module 220, data collection module 224, data processing module 226, or other operations of the automation computing system 116 may be performed at least in part by the GPUs 242.

FIG. 3 shows an example redundant power fabric for [PREAMBLE]. The redundant power fabric provides redundant pathways for power transfer between the power supplies 215, the sensors 212, and the CPU packages 204. In this example, the power supplies 215 are coupled to the sensors 212 and CPU packages via two switched fabrics 214 a and 214 b. The topology shown in FIG. 3 provides redundant pathways between the power supplies 215, the sensors 212, and the CPU packages 204 such that power can be rerouted through any of multiple pathways in the event of a failure in an active connection pathway. The switched fabrics 214 a and 214 b may provide power to the sensors 212 using various connections, including Mobile Industry Processor Interface (MIPI), Inter-Integrated Circuit (I2C), Universal Serial Bus (USB), or another connection. The switched fabrics 214 a and 214 b may also provide power to the CPU packages 204 using various connections, including Peripheral Component Interconnect Express (PCIe), USB, or other connections. Although only two switched fabrics 214 a and 214 b are shown connecting the power supplies 215 to the sensors 212 and CPU packages 204, the approach shown by FIG. 3 can be modified to include three, four, five, or more switched fabrics 214.

FIG. 4 is an example redundant data fabric for [PREAMBLE]. The redundant data fabric provides redundant data connection pathways between sensors 212 and CPU packages 204. In this example view, three CPU packages 204 a, 204 b, and 204 c are connected to three sensors 212 a, 212 b, and 212 c via three switched fabrics 213 a, 213 b, and 213 c. Each CPU package 204 a, 204 b, and 204 c is connected to a subset of the switched fabrics 213 a, 213 b, and 213 c. For example, CPU package 204 a is connected to switched fabrics 213 a and 213 c, CPU package 204 b is connected to switched fabrics 213 a and 213 b, and CPU package 204 c is connected to switched fabrics 213 b and 213 c. Each switched fabric 213 a, 213 b, and 213 c is connected to a subset of the sensors 212 a, 212 b, and 212 c. For example, switched fabric 213 a is connected to sensors 212 a and 212 b, switched fabric 213 b is connected to sensor 212 b and 212 c, and switched fabric 213 c is connected to sensors 212 a and 212 c. Under this topology, each CPU package 204 a, 204 b, and 204 c has an available connection path to any sensor 212 a, 212 b, and 212 c. It is understood that the topology of FIG. 4 is exemplary, and that CPU packages, switched fabrics, sensors, or connections between components may be added or removed while maintaining redundancy.

FIG. 5 is an example view of process allocation across CPU packages for [PREAMBLE]. Shown are three CPU packages 204 a, 204 b, and 204 c. Each CPU package 204 a includes a processing unit that has been allocated (e.g., by a hypervisor 228 or other process or service) primary execution of a process and another processing unit that has been allocated secondary execution of a process. As set forth herein, primary execution of a process describes an executing instance of a process whose output will be provided to another process or service. Secondary execution of the process describes executing an instance of the process in parallel to the primary execution, but the output may not be output to the other process or service. For example, in CPU package 204 a, processing unit 502 a has been allocated secondary execution of “process B,” denoted as secondary process B 504 b, while processing unit 502 b has been allocated primary execution of “process C,” denoted as primary process C 506 a.

CPU package 204 a also comprises two redundant processing units that are not actively executing a process A, B, or C, but are instead reserved in case of failure of an active processing unit. Redundant processing unit 508 a has been reserved as “A/B redundant,” indicating that reserved processing unit 508 a may be allocated primary or secondary execution of processes A or B in the event of a failure of a processing unit allocated the primary or secondary execution of these processes. Redundant processing unit 508 b has been reserved as “A/C redundant,” indicating that reserved processing unit 508 b may be allocated primary or secondary execution of processes A or C in the event of a failure of a processing unit allocated the primary or secondary execution of these processes.

CPU package 204 b includes processing unit 502 c, which has been allocated primary execution of “process A,” denoted as primary process A 510 a, and processing unit 502 d, which has been allocated secondary execution of “process C,” denoted as secondary process C 506 a. CPU package 204 b also includes redundant processing unit 508 c, reserved as “A/B redundant,” and redundant processing unit 508 d, reserved as “B/C redundant.” CPU package 204 c includes processing unit 502 e, which has been allocated primary execution of “process B,” denoted as primary process B 504 a, and processing unit 502 f, which has been allocated secondary execution of “process A,” denoted as secondary process A 510 b. CPU package 204 c also includes redundant processing unit 508 e, reserved as “B/C redundant,” and redundant processing unit 508 f, reserved as “A/C redundant.”

As set forth in the example view of FIG. 5 , primary and secondary instances processes A, B, and C are each executed in an allocated processing unit. Thus, if a processing unit performing primary execution of a given process fails, the processing unit performing secondary execution may instead provide output of the given process to a receiving process or service. Moreover, the primary and secondary execution of a given process are executed on different CPU packages. Thus, if an entire processing unit fails, execution of each of the processes can continue using one or more processing units handling secondary execution. The redundant processing units 508 a—f allow for allocation of primary or secondary execution of a process in the event of processing unit failure. This further prevents errors caused by processing unit failure as parallel primary and secondary execution of a process may be restored. The number of CPU packages, processing units, redundant processing units, and processes may be modified according to performance requirements while maintaining redundancy.

For further explanation, FIG. 6 sets forth a diagram of an execution environment 227 in accordance with some embodiments of the present disclosure. The execution environment 227 depicted in FIG. 6 may be embodied in a variety of different ways. The execution environment 227 may be provided, for example, by one or more physical or virtual machine components consisting of bare-metal applications, operating systems such as Android, Linux, Real-time Operating systems (RTOS), Automotive RTOS, such as AutoSAR, and others, including combinations thereof. The execution environment 227 may also be provided by cloud computing providers such as Amazon AWS™, Microsoft Azure™, Google Cloud™, and others, including combinations thereof. Alternatively, the execution environment 227 may be embodied as a collection of devices (e.g., servers, storage devices, networking devices) and software resources that are included in a computer or distributed computer or private data center. The execution environment 227 may be constructed in a variety of other ways and may even include resources within one or more autonomous vehicles or resources that communicate with one or more autonomous vehicles.

The execution environment 227 depicted in FIG. 6 may include storage resources 608, which may be embodied in many forms. For example, the storage resources 608 may include flash memory, hard disk drives, nano-RAM, 3D crosspoint non-volatile memory, MRAM, non-volatile phase-change memory (PCM), storage class memory (SCM), or many others, including combinations of the storage technologies described above. Other forms of computer memories and storage devices may be utilized as part of the execution environment 227, including DRAM, SRAM, EEPROM, universal memory, and many others. The storage resources 608 may also be embodied, in embodiments where the execution environment 227 includes resources offered by a cloud provider, as cloud storage resources such as Amazon Elastic Block Storage (EBS) block storage, Amazon S3 object storage, Amazon Elastic File System (EFS) file storage, Azure Blob Storage, and many others. The example execution environment 227 depicted in FIG. 6 may implement a variety of storage architectures, such as block storage where data is stored in blocks, and each block essentially acts as an individual hard drive, object storage where data is managed as objects, or file storage in which data is stored in a hierarchical structure. Such data may be saved in files and folders, and presented to both the system storing it and the system retrieving it in the same format.

The execution environment 227 depicted in FIG. 6 also includes communications resources 610 that may be useful in facilitating data communications between components within the execution environment 227, as well as data communications between the execution environment 227 and computing devices that are outside of the execution environment 227. Such communications resources may be embodied, for example, as one or more routers, network switches, communications adapters, and many others, including combinations of such devices. The communications resources 610 may be configured to utilize a variety of different protocols and data communication fabrics to facilitate data communications. For example, the communications resources 610 may utilize Internet Protocol (‘IP’) based technologies, fibre channel (FC) technologies, FC over ethernet (FCoE) technologies, InfiniBand (TB) technologies, NVM Express (NVMe) technologies and NVMe over fabrics (NVMeoF) technologies, and many others. The communications resources 610 may also be embodied, in embodiments where the execution environment 227 includes resources offered by a cloud provider, as networking tools and resources that enable secure connections to the cloud as well as tools and resources (e.g., network interfaces, routing tables, gateways) to configure networking resources in a virtual private cloud. Such communications resources may be useful in facilitating data communications between components within the execution environment 227, as well as data communications between the execution environment 227 and computing devices that are outside of the execution environment 227 (e.g., computing devices that are included within an autonomous vehicle).

The execution environment 227 depicted in FIG. 6 also includes processing resources 612 that may be useful in useful in executing computer program instructions and performing other computational tasks within the execution environment 227. The processing resources 612 may include one or more application-specific integrated circuits (ASICs) that are customized for some particular purpose, one or more central processing units (CPUs), one or more digital signal processors (DSPs), one or more field-programmable gate arrays (FPGAs), one or more systems on a chip (SoCs), or other form of processing resources 612. The processing resources 612 may also be embodied, in embodiments where the execution environment 227 includes resources offered by a cloud provider, as cloud computing resources such as one or more Amazon Elastic Compute Cloud (EC2) instances, event-driven compute resources such as AWS Lambdas, Azure Virtual Machines, or many others.

The execution environment 227 depicted in FIG. 6 also includes software resources 613 that, when executed by processing resources 612 within the execution environment 227, may perform various tasks. The software resources 613 may include, for example, one or more modules of computer program instructions that when executed by processing resources 612 within the execution environment 227 are useful in training neural networks configured to determine control autonomous vehicle control operations. For example, a training module 614 may train a neural network using training data including sensor 212 data and control operations recorded or captured contemporaneous to the training data. In other words, the neural network may be trained to encode a relationship between an environment relative to an autonomous vehicle 100 as indicated in sensor 212 data and the corresponding control operations effected by a user or operation of the autonomous vehicle. The training module 614 may provide a corpus of training data, or a selected subset of training data, to train the neural network. For example, the training module 614 may select particular subsets of training data associated with particular driving conditions, environment states, etc. to train the neural network.

The software resources 613 may include, for example, one or more modules of computer program instructions that when executed by processing resources 612 within the execution environment 227 are useful in deploying software resources or other data to autonomous vehicles 100 via a network 618. For example, a deployment module 616 may provide software updates, neural network updates, or other data to autonomous vehicles 100 to facilitate autonomous vehicle control operations.

The software resources 613 may include, for example, one or more modules of computer program instructions that when executed by processing resources 612 within the execution environment 227 are useful in collecting data from autonomous vehicles 100 via a network 618. For example, a data collection module 620 may receive, from autonomous vehicles 100, collected sensor 212, associated control operations, software performance logs, or other data. Such data may facilitate training of neural networks via the training module 614 or stored using storage resources 608.

For further explanation, FIG. 7 sets forth a flow chart illustrating an exemplary method for anticipatory vehicle ECU actuation according to some embodiments of the present disclosure. The method of FIG. 7 may be performed, for example, in an autonomous vehicle such as an autonomous vehicle 100. Accordingly, in such embodiments, the method of FIG. 7 may be performed at least in part using an automation computing system 116 or other computing components of the autonomous vehicle 100. In some embodiments, the method of FIG. 7 may be performed in a non-autonomous vehicle (e.g., a vehicle lacking full autonomous driving functionality) having suitable cameras or other sensors facilitating the operations described herein. Accordingly, in such embodiments, the method of FIG. 7 may be performed at least in part using a vehicle control system (VCS), ECU, or other computing components of a vehicle.

The method of FIG. 7 includes determining 702, based on sensor data capturing an environment around a vehicle, a predicted environmental state corresponding to a future time. In some embodiments, the vehicle includes a stationary vehicle (e.g., parked). In some embodiments, the vehicle may be in motion. For example, where the vehicle is a non-autonomous vehicle, the vehicle may be under operator or driver control. As another example, where the vehicle is an autonomous vehicle, the vehicle may be under operator control in a non-autonomous driving mode or operating under an autonomous driving mode.

The sensor data capturing the environment around the vehicle may include video data from one or more camera sensors. For example, in some embodiments, the sensor data may include video data from a camera capturing video data from a particular side or facing of the vehicle (e.g., front side, left side, right side, or rear side). In some embodiments, the sensor data may include video data from multiple cameras. As an example, the sensor data may include video data from multiple cameras on a same side or facing of the vehicle, video data from multiple cameras on different sides or facings of the vehicle, or combinations thereof.

In some embodiments, the sensor data may include a current or most recent frame of video data. Where the sensor data includes video data from multiple cameras, the sensor data my include multiple current or most recent frames for each camera. In some embodiments, the sensor data may include one or more previous frames of video data. As an example, the sensor data may include the N-most recent frames of video data for each camera, a defined time window of most recent video data for each camera, and the like.

In some embodiments, determining 702 the predicted environmental state may include providing the sensor data to one or more models configured to provide, as output, a predicted environmental state. As an example, the one or more models may include models configured to identify particular visual objects or boundaries in the video data. As another example, the one or more models may include models configured to extrapolate or predict a future location or arrangement of identified objects. Continuing with this example, portions of video data over time capturing a particular object may be used to calculate directions of motion, velocity or motion vectors, and the like of objects relative to the vehicle in order to predict the location of the particular object at a future time. For example, while some objects may be stationary relative to their environment, such objects will appear to be in motion and change in distance relative to the vehicle by virtue of the vehicle being in motion. The one or more models may also be configured to identify particular road features or environmental conditions that may affect driving conditions, such as rain or wet roads, ice, inclines, declines, transitions in exterior lighting due to entering or exiting a shaded area (e.g., under an overpass, a tunnel), turns, hazards (e.g., debris, collision risks, potholes), and the like. Accordingly, the one or more models may be configured to determine, as part of the predicted environmental state, when or whether the vehicle will encounter these road or environmental conditions (e.g., based on a speed and direction of the vehicle, based on a defined driving route, and the like).

The predicted environmental state includes a state of the environment relative to the vehicle at the future time. Accordingly, the predicted environmental state may include predicted locations or placements of particular visual objects in the video data at the future time. Such objects may include other vehicles on the road, pedestrians, road signs, traffic signals, or other identifiable or perceptible objects. In other words, the predicted environmental state may describe where particular objects will be in the environment relative to the vehicle at the future time. The predicted environmental state may include particular locations or placements of various portions of a road or terrain, such as lane markers, road boundaries, curbs, and the like. As an example, the predicted environmental state may include the boundaries, directions, angles and the like of a road traversed by the vehicle.

In some embodiments, the predicted environmental state may include a predicted driving condition at the future time. As an example, the predicted driving condition may include a surface condition for a road at the future time. Continuing with this example, where ice, water, hazard, or other surfaces on a road are identified, the predicted environmental state may describe whether the vehicle will be encountering the particular surface condition at a particular time. As another example, the predicted driving condition may include an angle of a road, including an incline, decline, angle of curvature, and the like. As a further example, the predicted driving condition may include an amount of exterior light relative to the vehicle. For example, where the predicted environmental state includes entering a tunnel or traveling under an overpass, the predicted driving condition may include a predicted amount of exterior light by virtue of the vehicle being shaded from exterior light sources.

In some embodiments, the future time may be based on a particular interval or frequency for capturing video data. For example, assuming video data being captured at thirty frames per second, the future time may be N*(1/30) seconds ahead of a current time, with N being some number. In some embodiments, the future time may be calculated independent of the frequency of capturing video data. In some embodiments, the future time may be based on a speed of the vehicle. For example, where the vehicle is traveling at a slower speed, the future time may be further ahead in time. Where the vehicle is traveling at a faster speed, the future time may be sooner in time. The future time may also be calculated according to other criteria, such as traffic density, particular driving maneuvers being performed (e.g., automated or manual), and based on other factors.

The method of FIG. 7 also includes determining 704, based on the predicted environmental state, an actuation state for one or more ECUs of the vehicle. An ECU of a vehicle controls one or more electronic subsystems of the vehicle. Such ECUs may include an engine control module (ECM), a powertrain control module (PCM), a brake control module (BCM) (e.g., a brake system), a transmission control module (TCM), a central control module (CCM), a central timing module (CTM), a general electronic module (GEM), a body control module (BCM), a suspension control module (SCM), or other ECU.

The actuation state of the one or more ECUs describes a degree to which each ECU is activated, deactivated, actuated, and the like. For example, for a suspension control module, the actuation state may include a suspension state describing a tightness/stiffness or softness of the suspension. As another example, for an engine control module, the actuation state may include an acceleration state describing a degree to which acceleration is applied or released. As a further example, for a body control module, the actuation state may include a lock state describing a state of one or more locks (e.g., locked or unlocked), a window state describing a state of one or more windows (e.g., raised, lowered, partially lowered, and the like), a seat state describing particular configurable parameters for a seat, such as a seat stiffness, lumbar support area, heating or cooling control, haptic feedback, massage or vibration features, or other states. As an example, a seat stiffness may be described as a positioning or a tension of one or more tensioning members or tensioning devices in the seat that may be actuated to provide a particular amount of tension within the seat. Accordingly, in some embodiments, a plurality of such tensioning devices may be used to control a level of stiffness for various components or areas of the seat. As a further example, in some embodiments, the actuation state may describe a brightness of an interior display or instrument panel. In such an embodiment, the brightness of the interior display may be based on a detected or estimated amount of exterior light in the predicted environmental state. For example, the brightness of the interior display may be set in the actuation state based on some threshold or differential between exterior light and the interior display brightness.

The particular states described in the actuation state is dependent on which ECUs are affected according to the approaches set forth herein. The particular ECUs described by the actuation state may vary according to particular design considerations. Furthermore, the particular ECUs may depend on whether the vehicle is in motion or stationary. For example, ECUs such as a suspension control module, powertrain control module, and the like may be affected while the car is in motion, while ECUs such as the body control system (e.g., to control locks) may be affected while the car is stationary.

Determining 704 the actuation state may be based on one or more models that accept, as input, an environmental state and provide, as output, a corresponding actuation state. Accordingly, the environmental state provided as input to the one or more models is the determined 702 predicted environmental state. The one or more models may include one or more trained machine learning models. For example, assume that a model, or a network of one or more linked models, is trained using observed (e.g., non-predicted) environmental states and the actuation state of a vehicle at the time of the observed environmental state. In other words, particular environmental states and their corresponding actuation states are used to train the one or more models. For example, an actuation state to turn on headlights can occur when the one or more models detect that, when entering a low-light condition (e.g., tunnel, forest, rainy weather, garage), headlights are turned on by a driver. As another example, an actuation state to softer suspension can occur when the one or more model detects that, when bumpier road is present ahead (e.g., speed bump, off-road conditions, debris), a driver adjusts their suspension softer. Additional examples of environmental states and its associated actuation are discussed elsewhere in this application, which may be used with the one or more models to determine an actuation state. In an alternate embodiment, the actuation state may be provided as a list of actuation states when a predicted environmental state is encountered. Accordingly, in some embodiments, the particular models used to determine the actuation state may or may not be trained based on activity from other drivers or other vehicles. For example, in some embodiments, the particular models used to determine the actuation state may be trained by observing behavior for a particular driver or vehicle over time and then be used to determine the actuation state for that particular vehicle or driver. Accordingly, in some embodiments, training of such a model may be performed in the autonomous vehicle 100, in a remotely disposed computing environment by providing training data from the autonomous vehicle 100 to the remotely disposed computing environment, or combinations thereof.

As is described above, in some embodiments, determining 702 the predicted environmental state may be performed using one or more models accepting, as input, data including sensor data from various sensors of the autonomous vehicle 100. Accordingly, in some embodiments, the output of these one or more models (e.g., the predicted environmental state) may be provided as input to another one or more models that determine 704 the actuation state based on the predicted environmental state. In other embodiments, a single model may be used to both determine 702 the predicted environmental state and determine 704 the actuation state based on the predicted environmental state.

The method of FIG. 7 also includes configuring 706 the one or more ECUs according to the actuation state before the future time. The one or more ECUs are configured before the future time in that the ECUs are configured before reaching the future time corresponding to the predicted environmental state. Configuring 706 the one or more ECUs includes actuating the one or more ECUs based on the degree of actuation described in the actuation state. For example, in some embodiments, configuring 706 the one or more ECUs according to the actuation state includes actuating one or more of the ECUs to equal a degree of actuation described in the actuation state. As an example, where the actuation state describes a particular degree of suspension tightness, configuring 706 the one or more ECUs includes actuating a suspension control module to the degree of tightness indicated in the actuation state. As a further example, where the actuation state describes an activation state of headlights (e.g., on or off, degree of brightness), configuring 706 the one or more ECUs includes adjusting the headlights to a particular brightness or activation state. As a further example, where the actuations state describes a state of a display or interface such as a degree of brightness, configuring 706 the one or more ECUs includes adjusting the display brightness to the particular degree of brightness.

In some embodiments, configuring 706 the one or more ECUs according to the actuation state includes actuating one or more of the ECUs to reduce a delta or difference between a current degree of actuation and a degree of actuation described in the actuation state. For example, assume that a difference between a current degree of actuation and a degree of actuation described in the actuation state for a particular ECU exceeds some threshold. Thus, the degree of actuation for that ECU may not be changed directly to the degree of actuation described in the actuation state due to safety considerations, engineering considerations, occupant comfort experiences, and the like. Accordingly, the degree of actuation for that ECU may be modified to approach the degree of actuation described in the actuation state, thereby reducing the delta between a current degree of actuation and a degree of actuation described in the actuation state.

In some embodiments, configuring 706 the one or more ECUs may be based on one or more timing criteria. For example, where configuring 706 the one or more ECUs includes modifying an actuation state or brightness of an interior display or headlights, the one or more timing criteria may define how soon prior to entering an area of reduced brightness (e.g., a shaded area) the headlights should be activated or the display brightness adjusted. In some embodiments, these timing criteria may be predefined or based on operator (e.g., driver) activity. For example, in some embodiments, driver activity may be monitored to determine when a driver activates the vehicle headlights relative to a time in which the vehicle enters an area of reduced brightness. This activity may then be used to determine a timing criteria for automatically activating the headlights when it is predicted that the vehicle will enter the area of reduced brightness. As another example, where the vehicle is entering an area of increased brightness, the timing criteria may define when (e.g., before or after entering the area of increased brightness) the headlights should be deactivated or reduced in brightness, or when the display brightness should be reduced.

In some embodiments, after configuring 706 the one or more ECUs, the one or more ECUs may be reconfigured based on a later predicted or actual environmental state of the vehicle. For example, where the seat stiffness was increased in anticipation of entering a turn, the seat stiffness may be reconfigured to an original stiffness or another stiffness upon exiting the turn. As another example, where the suspension was tightened in anticipation of vehicle contact with a pothole, the suspension may be reconfigured to an original tightness or another tightness after contact with the pothole or after determining that the vehicle is no longer at risk of contacting the pothole.

Some solutions for ECU actuation are reactionary in that particular ECUs are actuated based on observed environmental or driving behavior. As an example, an anti-lock brake system (ABS) may be engaged in response to detecting a loss of traction when brakes are applied. As another example, a vehicle suspension may be tightened in response to the vehicle performing a turn. In other words, ECUs are activated in response to satisfying or reaching detected conditions for actuating the particular ECUs.

In contrast, the approaches for anticipatory vehicle ECU actuation described herein are predictive and anticipatory in that particular ECUs are actuated according to a predicted environmental state. As an example, assume that a vehicle is approaching a turn. The turn in the road may be identified in video data. Accordingly, the predicted environmental state for the vehicle may be determined to include entering or traveling on the turn at the future time. This predicted environmental state where the vehicle is entering the turn may be provided to one or models that provide, as output, an actuation state indicating that the suspension should be tightened or that a seat stiffness should be increased. In response to this actuation state, the suspension control module is actuated to tighten the suspension in anticipation of the vehicle entering the turn.

As another example, assume that the vehicle is approaching a decline in the road, with a predicted environmental state indicating that the vehicle will be traveling along the declined road. An actuation state may indicate that the accelerator should be released so that the vehicle may coast down the decline. In anticipation of the vehicle entering the decline, the engine control module may be actuated to reduce or end acceleration.

As an additional example, assume that the vehicle is approaching a tunnel, overpass, shadowed area, or other area where the environmental brightness decreases. In such an example, a shadow or darkened area may be identified in video data, and the predicted environmental state may indicate that the vehicle will enter the darkened area. Accordingly, the actuation state may indicate that headlights should be activated or increased in brightness (e.g., a headlight state), or may indicate that a brightness of an instrument panel should be increased (e.g., an instrument panel brightness). Accordingly, in anticipation of the vehicle entering the darkened area, one or more ECUs may be actuated to brighten the headlights, the instrument panel, or both.

As a further example with a stationary vehicle, assume that an operator or driver is walking towards a vehicle. A determined predicted environmental state may include the operator opening or attempting to open a door of the vehicle after unlocking the locks. Accordingly, a predicted environmental state may indicate that the locks are in an unlocked position. Accordingly, the body control module may be actuated to unlock one or more doors in anticipation of the operator approaching and opening the door of the vehicle. A predetermined environmental state may also include one or more other passengers (e.g., identified as approaching the vehicle) entering the vehicle. Accordingly, the body control module may be actuated to unlock passenger side doors or rear side doors. Moreover, in some embodiments, the passengers may be identified (e.g., from video data of external cameras) as children. Accordingly, in some embodiments, child safety locks may be anticipatorily activated to prevent the child passengers from exiting the car after entering. As a further example, where an operator or passenger is identified as carrying a large object or pushing cargo towards the vehicle, a trunk or hatch may be anticipatorily opened or unlocked.

For further explanation, FIG. 8 sets forth a flowchart of another example method for anticipatory vehicle ECU actuation according to some embodiments of the present disclosure. The method of FIG. 8 is similar to FIG. 7 in that the method of FIG. 8 also includes determining 702, based on sensor data capturing an environment around a vehicle, a predicted environmental state corresponding to a future time; determining 704, based on the predicted environmental state, an actuation state for one or more ECUs of the vehicle; and configuring 706 the one or more ECUs according to the actuation state before the future time.

The method of FIG. 8 differs from FIG. 7 in that determining 702, based on sensor data capturing an environment around a vehicle, a predicted environmental state corresponding to a future time includes determining 802 the predicted environmental state based on other sensor data from one or more non-camera sensors. In other words, the predicted environmental state may be determined based on a combination of sensor data from one or more camera sensors and other sensor data from the one or more non-camera sensors. The one or more non-camera sensors may include one or more non-camera sensors describing a motion, position, or location of the vehicle. Such non-camera sensors may include, for example, accelerometers, GPS sensors, and the like.

The predicted environmental state may then be determined using similar approaches as described above but using the other sensor data. For example, the other sensor data from the non-camera sensors may be provided in addition to the video sensor data from the one or more camera sensors as input to one or more models that provide, as output, a predicted environmental state. The other sensor data may provide a more accurate prediction of the environmental state relative to the vehicle. As an example, accelerometer data describing a speed or acceleration of the vehicle may be used to more accurately represent the predicted location of objects in the environment. As another example, gyroscope data, or a time series of object data, may be used to more accurately predict the location of objects in the environment by virtue of providing a more accurate representation of the vehicle angle at the future time.

In view of the explanations set forth above, the benefits of anticipatory ECU actuation according to embodiments of the present invention include improved performance of a vehicle by anticipatorily actuating various ECUs based on predicted environmental conditions.

Exemplary embodiments of the present disclosure are described largely in the context of a fully functional computer system for anticipatory vehicle ECU actuation. The present disclosure also may be embodied in a computer program product disposed upon computer readable storage media for use with any suitable data processing system. Such computer readable storage media may be any storage medium for machine-readable information, including magnetic media, optical media, or other suitable media. Examples of such media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others. Any computer system having suitable programming means will be capable of executing the steps of the method of the disclosure as embodied in a computer program product. Although some of the exemplary embodiments described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative embodiments implemented as firmware or as hardware are well within the scope of the present disclosure.

The present disclosure may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein may be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

It will be understood that any of the functionality or approaches set forth herein may be facilitated at least in part by artificial intelligence applications, including machine learning applications, big data analytics applications, deep learning, and other techniques. Applications of such techniques may include: machine and vehicular object detection, identification and avoidance; visual recognition, classification and tagging; algorithmic financial trading strategy performance management; simultaneous localization and mapping; predictive maintenance of high-value machinery; prevention against cyber security threats, expertise automation; image recognition and classification; question answering; robotics; text analytics (extraction, classification) and text generation and translation; and many others.

It will be understood from the foregoing description that modifications and changes may be made in various embodiments of the present disclosure without departing from its true spirit. The descriptions in this specification are for purposes of illustration only and are not to be construed in a limiting sense. The scope of the present disclosure is limited only by the language of the following claims. 

What is claimed is:
 1. A method comprising: determining, based on sensor data capturing an environment around a vehicle, a predicted environmental state corresponding to a future time and comprising a transition in exterior lighting; determining, based on the predicted environmental state, an actuation state for one or more headlights of the vehicle; configuring, based on one or more timing criteria based on previous operator activity, the one or more headlights of the vehicle according to the actuation state before the future time; determining, based on the predicted environmental state, a brightness for one or more interior displays of the vehicle; and configuring, based on the one or more timing criteria, the one or more interior displays of the vehicle according to the determined brightness before the future time.
 2. The method of claim 1, wherein the sensor data comprises video data.
 3. The method of claim 2, wherein determining the predicted environmental state further comprises determining the predicted environmental state based on other sensor data from one or more non-camera sensors.
 4. An apparatus comprising one or more processors and memory storing instructions that, when executed, cause the one or more processors to perform steps comprising: determining, based on sensor data capturing an environment around a vehicle, a predicted environmental state corresponding to a future time and comprising a transition in exterior lighting; determining, based on the predicted environmental state, an actuation state for one or more headlights of the vehicle; configuring, based on one or more timing criteria based on previous operator activity, the one or more headlights of the vehicle according to the actuation state before the future time; determining, based on the predicted environmental state, a brightness for one or more interior displays of the vehicle; and configuring, based on the one or more timing criteria, the one or more interior displays of the vehicle according to the determined brightness before the future time.
 5. The apparatus of claim 4, wherein the sensor data comprises video data.
 6. The apparatus of claim 5, wherein determining the predicted environmental state further comprises determining the predicted environmental state based on other sensor data from one or more non-camera sensors.
 7. A computer program product disposed upon a non-transitory computer readable medium, the computer program product comprising computer program instructions that, when executed, cause a computer system of the autonomous vehicle to carry out the steps of: determining, based on sensor data capturing an environment around a vehicle, a predicted environmental state corresponding to a future time and comprising a transition in exterior lighting; determining, based on the predicted environmental state, an actuation state for one or more headlights of the vehicle; configuring, based on one or more timing criteria based on previous operator activity, the one or more headlights of the vehicle according to the actuation state before the future time; determining, based on the predicted environmental state, a brightness for one or more interior displays of the vehicle; and configuring, based on the one or more timing criteria, the one or more interior displays of the vehicle according to the determined brightness before the future time.
 8. The computer program product of claim 7, wherein the sensor data comprises video data.
 9. The computer program product of claim 8, wherein determining the predicted environmental state further comprises determining the predicted environmental state based on other sensor data from one or more non-camera sensors. 